Neighborhood Structural Similarity Mapping for the Classification of Masses in Mammograms

A Parallel Spiking Neural Network Based on Adaptive Lateral Inhibition Mechanism for Objective Recognition

Spiking neural network (SNN) has attracted extensive attention in the field of machine learning because of its biological interpretability and low power consumption. However, the accuracy of pattern recognition cannot completely surpass deep neural networks (DNNs). The main reason is that the inherent nondifferentiability of spiking neurons makes SNN unable to be trained directly by the gradient descent algorithm, and there is also no unified training algorithm for SNN. Inspired by the biological vision system, this paper proposes a parallel convolution SNN structure combined with an adaptive lateral inhibition mechanism. And, a way of dynamically evolving the time constant with the training of SNN is proposed to ensure the diversity of neurons. This paper verifies the effectiveness of the proposed methods on static datasets and neuromorphic datasets and extends it to the recognition of breast tumors. Experimental results show that the SNN has obvious advantages in dynamical datasets. For breast tumors, it is also an edge-based task, because the edge of a medical image contains the most important information in the image. This kind of information can provide great help for the noninvasive and accurate diagnosis of diseases. The Experimental results show that the proposed method is very close to the recognition results of DNNs on static datasets, and its performance on neuromorphic datasets exceeds that of DNNs.

Deep Learning Capabilities for the Categorization of Microcalcification

Breast cancer is the most common cancer in women worldwide. It is the most frequently diagnosed cancer among women in 140 countries out of 184 reporting countries. Lesions of breast cancer are abnormal areas in the breast tissues. Various types of breast cancer lesions include (1) microcalcifications, (2) masses, (3) architectural distortion, and (4) bilateral asymmetry. Microcalcification can be classified as benign, malignant, and benign without a callback. In the present manuscript, we propose an automatic pipeline for the detection of various categories of microcalcification. We performed deep learning using convolution neural networks (CNNs) for the automatic detection and classification of all three categories of microcalcification. CNN was applied using four different optimizers (ADAM, ADAGrad, ADADelta, and RMSProp). The input images of a size of 299 × 299 × 3, with fully connected RELU and SoftMax output activation functions, were utilized in this study. The feature map was obtained using the pretrained InceptionResNetV2 model. The performance evaluation of our classification scheme was tested on a curated breast imaging subset of the DDSM mammogram dataset (CBIS–DDSM), and the results were expressed in terms of sensitivity, specificity, accuracy, and area under the curve (AUC). Our proposed classification scheme outperforms the ability of previously used deep learning approaches and classical machine learning schemes.

Discriminative Localized Sparse Approximations for Mass Characterization in Mammograms

The most common form of cancer among women in both developed and developing countries is breast cancer. The early detection and diagnosis of this disease is significant because it may reduce the number of deaths caused by breast cancer and improve the quality of life of those effected. Computer-aided detection (CADe) and computer-aided diagnosis (CADx) methods have shown promise in recent years for aiding in the human expert reading analysis and improving the accuracy and reproducibility of pathology results. One significant application of CADe and CADx is for breast cancer screening using mammograms. In image processing and machine learning research, relevant results have been produced by sparse analysis methods to represent and recognize imaging patterns. However, application of sparse analysis techniques to the biomedical field is challenging, as the objects of interest may be obscured because of contrast limitations or background tissues, and their appearance may change because of anatomical variability. We introduce methods for label-specific and label-consistent dictionary learning to improve the separation of benign breast masses from malignant breast masses in mammograms. We integrated these approaches into our Spatially Localized Ensemble Sparse Analysis (SLESA) methodology. We performed 10- and 30-fold cross validation (CV) experiments on multiple mammography datasets to measure the classification performance of our methodology and compared it to deep learning models and conventional sparse representation. Results from these experiments show the potential of this methodology for separation of malignant from benign masses as a part of a breast cancer screening workflow.

Deep Learning Based Capsule Neural Network Model for Breast Cancer Diagnosis Using Mammogram Images

Breast cancer is a commonly occurring disease in women all over the world. Mammogram is an efficient technique used for screening and identification of abnormalities over the breast region. Earlier identification of breast cancer enhances the prognosis of patients and is mainly based on the experience of the radiologist in interpretation of mammogram with quality of image. The advent of Deep Learning (DL) and Computer Vision techniques is widely used to perform breast cancer diagnosis. This paper presents a new Optimal Multi-Level Thresholding-based Segmentation with DL enabled Capsule Network (OMLTS-DLCN) breast cancer diagnosis model utilizing digital mammograms. The OMLTS-DLCN model involves an Adaptive Fuzzy based median filtering (AFF) technique as a pre-processing step to eradicate the noise that exists in the mammogram images. Besides, Optimal Kapur's based Multilevel Thresholding with Shell Game Optimization (SGO) algorithm (OKMT-SGO) is applied for breast cancer segmentation. In addition, the proposed model involves a CapsNet based feature extractor and Back-Propagation Neural Network (BPNN) classification model is employed to detect the existence of breast cancer. The diagnostic outcomes of the presented OMLTS-DLCN technique is examined by means of benchmark Mini-MIAS dataset and DDSM dataset. The experimental values obtained highlights the superior performance of the OMLTS-DLCN model with a higher accuracy of 98.50 and 97.55% on the Mini-MIAS dataset and DDSM dataset, respectively.

Thermography as an Economical Alternative Modality to Mammography for Early Detection of Breast Cancer

Breast cancer has become a menacing form of cancer among women accounting for 11.6% of total deaths of 9.6 million due to all types of cancer every year all over the world. Early detection increases chances of survival and reduces the cost of treatment as well. Screening modalities such as mammography or thermography are used to detect cancer early; thus, several lives can be saved with timely treatment. But, there are interpretational failures on the part of the radiologists to read the mammograms or thermograms and also there are interobservational and intraobservational differences between them. So, the degree of variations among the different radiologists in the interpretation of results is very high resulting in false positives and false negatives. The double reading can reduce the human errors involved in the interpretation of mammograms. But, the limited number of medical professionals in developing or underdeveloped countries puts a limitation on this remedial way. So, a computer-aided system (CAD) is proposed to detect the benign cases from the abnormal cases that can result in automatic detection of breast cancer or can provide a double reading in the case of nonavailability of the trained medical professionals in developing economies. The generally accepted screening modality is mammography for the early detection of cancer. But thermography has been tried for early detection of breast cancer in recent times. The high metabolic activity of the cancer cells results in an early change in the temperature profile of the region. This shows asymmetry between normal and cancerous breast which can be detected using different techniques. Thus, this work is focussed on the use of thermography in the early detection of breast cancer. An experimental study is conducted to find the results of classification accuracy to compare the efficacy of thermography and mammography in classifying the normal from abnormal ones and further abnormal ones into benign and malignant cases. Thermography is found to have classification accuracy almost at par with mammography for classifying the cancerous breasts from healthy ones with classification accuracies of thermography and mammography being 96.57% and 98.11%, respectively. Thermography is found to have much better accuracy in identifying benign cases from the malignant ones with the classification accuracy of 92.70% as compared to 82.05% with mammography. This will result in the early detection of cancer. The advantage of being portable and inexpensive makes thermography an attractive modality to be used in economically backward rural areas where mammography is not practically possible.

Formulation of probability-based pervasive information set features and Hanman transform classifier for the categorization of mammograms

With aim of detecting breast cancer at the early stages using mammograms, this study presents the formulation of five feature types by extending the information set to encompass the concept of an intuitionist fuzzy set. The resulting pervasive information set gives not only the certainty of the pixel intensities of mammograms to a class but also the deficiency in the fuzzy modeling referred to as the hesitancy. The generalized adaptive Hanman Anirban fuzzy entropy function is shown to be equivalent to the hesitancy entropy function. The probability-based fuzzy Hanman transform and the pervasive Information with probability taking the role of hesitancy degree help derive the above five feature types termed as probability-based pervasive Information set features. The effectiveness of each feature type is demonstrated on the mini-MIAS and DDSM databases for the multi-class categorization of mammograms using the Hanman transform classifier. The statistical analysis by ANOVA test proves that the features are statistically significant and the experimental results are shown to be clinically relevant by the expert radiologists. The performance of the five feature types is either superior to or equal to that of some deep learning architectures on comparison but they outperform the state-of-the-art literature methods in the classification of breast cancer using mammograms.

Spatially localized sparse representations for breast lesion characterization

RATIONALE

The topic of sparse representation of samples in high dimensional spaces has attracted growing interest during the past decade. In this work, we develop sparse representation-based methods for classification of radiological imaging patterns of breast lesions into benign and malignant states.

METHODS

We propose a spatial block decomposition method to address irregularities of the approximation problem and to build an ensemble of classifiers (CL) that we expect to yield more accurate numerical solutions than conventional whole-region of interest (ROI) sparse analyses. We introduce two classification decision strategies based on maximum a posteriori probability (BBMAP-S), or a log likelihood function (BBLL-S).

RESULTS

To evaluate the performance of the proposed approach we used cross-validation techniques on imaging datasets with disease class labels. We utilized the proposed approach for separation of breast lesions into benign and malignant categories in mammograms. The level of difficulty is high in this application and the accuracy may depend on the lesion size. Our results indicate that the proposed integrative sparse analysis addresses the ill-posedness of the approximation problem, producing AUC (area under the receiver operating curve) value of 89.1% for randomized 30-fold cross-validation.

CONCLUSIONS

Furthermore, our comparative experiments showed that the BBLL-S decision function may yield more accurate classification than BBMAP-S because BBLL-S accounts for possible estimation bias.

A New Computer-Aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms

Mammography remains the most prevalent imaging tool for early breast cancer screening. The language used to describe abnormalities in mammographic reports is based on the Breast Imaging Reporting and Data System (BI-RADS). Assigning a correct BI-RADS category to each examined mammogram is a strenuous and challenging task for even experts. This paper proposes a new and effective computer-aided diagnosis (CAD) system to classify mammographic masses into four assessment categories in BI-RADS. The mass regions are first enhanced by means of histogram equalization and then semiautomatically segmented based on the region growing technique. A total of 130 handcrafted BI-RADS features are then extracted from the shape, margin, and density of each mass, together with the mass size and the patient's age, as mentioned in BI-RADS mammography. Then, a modified feature selection method based on the genetic algorithm (GA) is proposed to select the most clinically significant BI-RADS features. Finally, a back-propagation neural network (BPN) is employed for classification, and its accuracy is used as the fitness in GA. A set of 500 mammogram images from the digital database for screening mammography (DDSM) is used for evaluation. Our system achieves classification accuracy, positive predictive value, negative predictive value, and Matthews correlation coefficient of 84.5%, 84.4%, 94.8%, and 79.3%, respectively. To our best knowledge, this is the best current result for BI-RADS classification of breast masses in mammography, which makes the proposed system promising to support radiologists for deciding proper patient management based on the automatically assigned BI-RADS categories.

Automated Classification of Malignant and Benign Breast Cancer Lesions Using Neural Networks on Digitized Mammograms

We propose a novel neural network approach for the classification of abnormal mammographic images into benign or malignant based on their texture representations. The proposed framework has the capability of mapping high dimensional feature space into a lower-dimension, in a supervised way. The main contribution of the proposed classifier is to introduce a new neuron structure for map representation and adopt a supervised learning technique for feature classification. This is achieved by making the weight updating procedure dependent on the class reliability of the neuron. We showed high accuracy (95.2%) for our proposed approach in the classification of abnormal real mammographic images when compared to other related methods.